However, it seems by the technical readout and from reviews that the ultra-wide lens and sensor is the same one as in al

While computational photography can help a lot with collecting more light over more shots, higher ISO can also help freeze motion better; this makes this iPhone uniquely more suited for capturing action in lower light. You can do a lot in software, but not everything.

iPhones tend to get better every year even when not changing their sensors and lenses; the real leaps in camera performance are made in computational photography, with merging many images and using AI and machine learning to augment the tiny sensors that fit into these magical devices we hold in our hands.

Bayes’ Theorem allows us to overcome our incorrect intuitions about conditional probability in a logical, straightforward manner. Its applications are real and varied, ranging from understanding our test results (with real-world consequences) to improving our machine learning models. I hope this guide was useful and illuminated some of the counterintuitive aspects of Bayes’.

A new sensor means new specs. Paired with the new, faster aperture the iPhone 12 gets across the line of ƒ/1.6, the iPhone Pro Max does not just gain low-light ability with its sheer sensor size or new in-body sensor-shift stabilization: the maximum ISO sensitivity of the sensor is up 85% to a maximum ISO of 7616.

The technical readout confirms we’re going to about a 65mm (Full-frame equivalent) telephoto lens on the rear, with a slight sacrifice to light-collecting ability: we lose our nice ƒ/2.0 aperture and go back to the iPhone XS and X telephoto-standard ƒ/2.2. Not too terrible.

It’s that time again. Every year, we take a deep dive into what is new in the iPhone camera. This year is special: Apple is introducing several new cameras in the iPhone 12 and a whole new sensor in the iPhone 12 Pro Max.

We’ll be doing a thorough analysis of the iPhone 12 and 12 Pro Max cameras, comparing them to previous iPhones and tearing down the new processing in Smart HDR 3 and the benefits of their new lenses. First, however, we’ll take a quick look at what is new in the iPhone 12 Pro Max.

The iPhone 12 Pro Max is the first iPhone in a while to get a bigger sensor. Not just a bit bigger: the sensor is a whopping 47% larger, which should not just help it in low light but also allow for more depth of field, more detail in photos and generally a different ‘look’.

Bayes’ Theorem allows us to overcome our incorrect intuitions about conditional probability in a logical, straightforward manner. Its applications are real and varied, ranging from understanding our test results (with real-world consequences) to improving our machine learning models. I hope this guide was useful and illuminated some of the counterintuitive aspects of Bayes’.

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f you are planning to purchase the program, it’s best that you do dynamic warmups for a good 10 minutes to raise your heart rate near your fat-burning zone so you don’t spend the first 15 minutes of the exercise trying to raise your body’s temperature).

Given that it’s raining, what is the probability that a casual rider will rent a bike? Given high temperatures, what is the probability of good weather? How does that affect ridership? These questions can be approached in many ways, but hopefully applying Bayes’ theorem will now be an option you consider! Understanding A/B testing, for example, is a great application.

Bayes’ can be leveraged to better understand your data. As an example, I’ve been exploring this popular Bike Sharing Ridership dataset. In it we have various features related to weather conditions that you could use to ask interesting questions and drive business understanding.

There was an interesting and controversial article released in 2005 by John Ioannidis titled, “Why Most Published Research Findings Are False”. In it, he uses Bayes’ Theorem to argue that, much like in our example above, the probability that a phenomenon is true given a positive research result is much lower than we think. This can be dangerous because we may be taking research as fact without additional verification. It also highlights an important issue in the scientific community, mainly that there is little funding/interest in replicating previous studies.

As humans, we’re pretty bad at understanding the relative size of numbers especially as the order of magnitude increases. What’s happening here is that we are seeing 95% true positive and not realizing that 95% of a small number is less than 5% of a much larger number! This is what P(pos) is capturing. Given a positive test, you are much more likely to be healthy and receive a false positive than you are to have the disease (it’s only 1/1000 after all) and get a positive test.

I’ll link this excellent overview by Jason Brownlee. Naive Bayes is a very common classification model that calculates P(class|data), or probability of a class given the data. Bayes Optimal Classifier can be used to predict on new observations given previous training data. Bayesian optimization can be used to fine-tune hyperparameters of ML models. Brownlee covers all these and more in the guide, and he does it better than I could.